Development of a Spatial Decision Support System for Farms Management الصادق عبدالله الجاك فضل الله

Mechanized agricultural operations are needed to increase productivity, efficiency and competitiveness of farms. Likewise, developing and promoting appropriate new technologies are needed in the decision making and management process. Farm decision-makers are faced with not only the need to manage traditional facilities, but, also, complex environmental management requirements. This research project aims to develop a conceptual model for a farm Spatial Decision Support System. The model uses Decision Support System (DSS) and Geographical Information System (GIS) to handle farm spatial and attribute information. The real power of GIS derives from the integration of database and graphics capabilities with engineering design, analytical, and cost models. Importance of research stems from its association with the community of Al-Kharj area. It is expected that the proposed system will be an effective tool for the advance of decision-making process on farms, and will contain many types of descriptive, quantitative and spatial analysis models, that can be easily and friendly used.

Multi-channel multi radio wireless communications technologies have changed the trend of traditional routing based Quality of Service (QoS) in wireless-mesh networks. This avalanche of growth in multimedia communication is trigged by Multi-Channel Multi-Radio Wireless Mesh Networks (MCMR-WMNs).

This research investigates the potential utility of chaotic dynamics in neural information processing. A novel chaotic spiking neural network model is presented which is composed of non-linear dynamic state (NDS) neurons. The activity of each NDS neuron is driven by a set of non-linear equations coupled with a threshold based spike output mechanism. If time-delayed self-connections are enabled then the network stabilises to a periodic pattern of activation. Previous publications of this work have demonstrated that the chaotic dynamics which drive the network activity ensure that an extremely large number of such periodic patterns can be generated by this network. This paper presents a major extension to this model which enables the network to recall a pattern of activity from a selection of previously stabilised patterns.

This research is concerned with using nonlinear dynamics to greatly enhance the range of possible behaviours of artificial neurons. A novel neuron model is presented which has a dynamic internal state defined by a set of nonlinear equations, together with a threshold driven spike output mechanism. With the aid of spike feedback control the model is able to stabilise one of a large number of Unstable Periodic Orbits in its internal dynamics. These orbits correspond to dynamic states of the neuron each of which generates a ...

Bioinformatics and computational biology (BCB) is a rapidly developing Multidisciplinary field which covers a wide range of areas, including genomic sequence alignments. It is a main tool in molecular biology in searching for similarity between sequences. Sequence alignment has many important real world applications. For example, if some information about one of the sequences is already known (e.g., the sequence represents a cancerous gene) then this information could be transferred to the other unknown sequences, which could be vital in early disease diagnosis and drug engineering. Other applications include the study of evolutionary development, forensic, and the history of species and their groupings. With the wide growth of genomic data, searching for a sequence homology over huge databases (often measured in gigabytes) is unable to produce results within a realistic time, hence the need for acceleration. Since the exponential increase of biological databases as a result of the human genome project (HGP), supercomputers and other parallel architectures such as the special purpose Very Large Scale Integration (VLSI) chip, Graphic Processing Unit (GPUs) and Field Programmable Gate Arrays (FPGAs) have become popular acceleration platforms. FPGAs generally offer more flexibility, higher performance and lower overheads. Nonetheless, the number of researchers working on FPGA-based accelerators for sequence alignment and BCB applications in general, remains low. This is due mainly to the relative newness of the two areas, but more importantly, perhaps, to the knowledge gap between bioinformaticians and molecular biologists on the one side and hardware design engineers on the other side. In...

One of the focal objectives of molecular genetics is the determination of the genetic basis of human disease. Respectable advancement has been made in this respect as of late; most prominently the production of the human genome in 2001, an exertion that took two decades to finish. However, new genome sequencing technology is getting available that will drastically decrease the measure of time it takes to get sequence data from a sample of DNA. These developments will permit investigations of human variety at the genetic level. Such studies hold incredible guarantee for enabling important discoveries in figuring out which nucleotides and genes in the human genome hold data about human susceptibility to specific disease. There are significant computing challenges connected with the new sequencing products that emerge because of the immense volumes of data that these machines produce. The human genome comprises of roughly 3 billion base-pairs. The most costly computational task in genome sequencing (utilizing the prevailing Whole Genome Shotgun Sequencing approach) is the alignment of short fragments of the genome being sequenced against a reference genome. There may be several million of such fragments that need to be aligned so as to sequence a single genome. Current methodologies to tackle this issue utilizing traditional PCs may take tens to countless CPU hours to complete the alignment necessities for sequencing of a single human genome. Even high performance cluster computers with huge numbers of CPUs may take several weeks to complete the fundamental calculations. A reconfigurable PC is a...

This project presents a systematic methodology for exploring all possible processor arrays of scalable and unified radix 2 modular Montgomery multiplication algorithm over either of the finite fields GF(P) and GF(2n). In this methodology, the algorithm is first expressed as a regular iterative expression, then the algorithm data dependence graph and a suitable affine scheduling function are obtained. Depending on the obtained function, Several possible processor arrays will be obtained and analyzed in terms of speed, area, and power consumption. The resulting processor arrays will be compared to other previously published efficient ones in terms of area, speed, and power consumption.

A Bivalent-Profiling Using an Ontology Base for Semantic-Based Search حكمت عوض عبدالجابر

Keeping track of user preferences in user profile may help search for retrieving relevant information. Nevertheless, users are still not satisfied with search results that match their interests. Semantic Web provides a meaning to Web content which plays a central role for knowledge management in user profiling, hence enables agents to search, find, extract, share and integrate information more easily. Using ontologies for semantic-based searching is likely the key solution. Adopting a bivalent-profiling approach and using ontology base characteristics, we propose a strategy to find the exact meaning of the query terms that can be exploited to expand the query in order to present customized information for individual users. This research studies the Semantic Web approach to represent knowledge, specifically in user preferences, and its consequences in changing the functionalities of the search agent. Additionally, an approach to adapt the user profile based on reasoning from the ontology base is introduced. The main contribution of this work is profiling, since the focus is on a strategy adopting bivalent-profile using an ontology base for semantic-based search. Finally, evaluation and conclusion are highlighted.
...

A weighted-profiling using an ontology base for semantic-based search حكمت عوض عبدالجابر

The information on the Web increases tremendously. A number of search engines have been developed for searching Web information and retrieving relevant documents that satisfy the inquirers needs. Search engines provide inquirers irrelevant documents among search results, since the search is text-based rather than semantic-based. Information retrieval research area has presented a number of approaches and methodologies such as profiling, feedback, query modification, human-computer interaction, etc for improving search results. Moreover, information retrieval has employed artificial intelligence techniques and strategies such as machine learning heuristics, tuning mechanisms, user and system vocabularies, logical theory, etc for capturing user's preferences and using them for guiding the search based on the semantic analysis rather than syntactic analysis. Although a valuable improvement has been recorded on search results, the survey has shown that still search engines users are not really satisfied with their search results. Using ontologies for semantic-based searching is likely the key solution. Adopting profiling approach and using ontology base characteristics, this work proposes a strategy for finding the exact meaning of the query terms in order to retrieve relevant information according to user needs. The evaluation of conducted experiments has shown the effectiveness of the suggested methodology and conclusion is presented.
...